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Handling of incomplete data sets using ICA and SOM in data mining

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Abstract

Based on independent component analysis (ICA) and self-organizing maps (SOM), this paper proposes an ISOM-DH model for the incomplete data’s handling in data mining. Under these circumstances the data remain dependent and non-Gaussian, this model can make full use of the information of the given data to estimate the missing data and can visualize the handled high-dimensional data. Compared with mixture of principal component analyzers (MPCA), mean method and standard SOM-based fuzzy map model, ISOM-DH model can be applied to more cases, thus performing its superiority. Meanwhile, the correctness and reasonableness of ISOM-DH model is also validated by the experiment carried out in this paper.

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Authors and Affiliations

  1. Department of Applied Mathematics, Sun Yat-sen University, Guangzhou, 510275, China

    Hongyi Peng & Siming Zhu

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  1. Hongyi Peng
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  2. Siming Zhu
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Correspondence to Hongyi Peng.

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Peng, H., Zhu, S. Handling of incomplete data sets using ICA and SOM in data mining. Neural Comput & Applic 16, 167–172 (2007). https://doi.org/10.1007/s00521-006-0058-6

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  • DOI: https://doi.org/10.1007/s00521-006-0058-6

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